Storytelling, in oral, visual, or written forms, plays a central role in various types of media, including novels, movies, television, and theater. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -the ability to organize experience into narrative form -is central to the cognitive processes employed across a range of experiences, from entertainment to active learning. It follows that computational systems possessing narrative intelligence may be able to interact with human users naturally because they understand collaborative contexts as emerging narrative and are able to express themselves by telling stories. A number of narrative intelligence tasks have been studied from a computational perspective including story understanding, story generation, and commonsense reasoning.One of the most compelling applications of narrative intelligence is the prospect of interactive narrative. Interactive narrative is a form of digital interactive experience in which users create or influence a dramatic storyline through actions, either by assuming the role of a character in a fictional virtual world, issuing commands to computer-controlled characters, or directly manipulating the fictional world state. It is most often considered as a form of interactive entertainment but can also be used for serious applications such as education and training. The most common form of interactive narrative involves the user taking on the role of the protagonist in an unfolding storyline.

Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agent's current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, -LRTA* , SLA*, and γ-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks. MotivationIn this paper, we consider a simultaneous planning and learning problem. One motivating application lies with navigation on an initially unknown map under real-time constraints. As an example, consider a robot driving to work every morning. Imagine the robot to be a newcomer to the town. The first route the robot finds may not be optimal because traffic jams, road conditions, and other factors are initially unknown. With the passage of time, the robot continues to learn and eventually converges to a nearly optimal commute. Note that planning and learning happen while the robot is driving and therefore are subject to time constraints.Present-day mobile robots are often plagued by localization problems and power limitations, but their simulation counter-parts already allow researchers to focus on the planning and learning problem. For instance, the RoboCup Rescue simulation league (Kitano, Tadokoro, Noda, Matsubara, Takahashi, Shinjou, & Shimada, 1999) requires real-time planning and learning with multiple agents mapping out an unknown terrain. Pathfinding is done in real time as various crises, involving fire spread and human victims trapped in rubble, progress while the agents plan.Similarly, many current-generation real-time strategy games employ a priori known maps. Full knowledge of the maps enables complete search methods such as A...

In recent years, the fields of Interactive Storytelling and Player Modelling have independently enjoyed increased interest in both academia and the computer games industry. The combination of these technologies, however, remains largely unexplored. In this paper, we present PaSSAGE (Player-Specific Stories via Automatically Generated Events), an interactive storytelling system that uses player modelling to automatically learn a model of the player's preferred style of play, and then uses that model to dynamically select the content of an interactive story. Results from a user study evaluating the entertainment value of adaptive stories created by our system as well as two fixed, pre-authored stories indicate that automatically adapting a story based on learned player preferences can increase the enjoyment of playing a computer role-playing game for certain types of players.

Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.

Heuristic search is a core area of Artificial Intelligence, successfully applied to planning, constraint satisfaction and game playing. In real-time heuristic search autonomous agents interleave planning and plan execution and access environment locally which make them more suitable for Artificial Life style settings. Over the last two decades a large number of real-time heuristic search algorithms have been manually crafted and evaluated. In this paper we break down several published algorithms into building blocks and then let a simulated evolution re-combine the blocks in a performancebased way. Remarkably, even relatively short evolution runs result in algorithms with state-of-the-art performance. These promising preliminary results open exciting possibilities in the field of real-time heuristic search.